Large-scale network analysis of the cerebrospinal fluid proteome identifies molecular signatures of frontotemporal lobar degeneration

Abstract

The pathophysiological mechanisms driving disease progression of frontotemporal lobar degeneration (FTLD) and corresponding biomarkers are not fully understood. We leveraged aptamer-based proteomics (> 4,000 proteins) to identify dysregulated communities of co-expressed cerebrospinal fluid proteins in 116 adults carrying autosomal dominant FTLD mutations (C9orf72, GRN, MAPT) compared to 39 noncarrier controls. Network analysis identified 31 protein co-expression modules. Proteomic signatures of genetic FTLD clinical severity included increased abundance of RNA splicing (particularly in C9orf72 and GRN) and extracellular matrix (particularly in MAPT) modules, as well as decreased abundance of synaptic/neuronal and autophagy modules. The generalizability of genetic FTLD proteomic signatures was tested and confirmed in independent cohorts of 1) sporadic progressive supranuclear palsy-Richardson syndrome and 2) frontotemporal dementia spectrum syndromes. Network-based proteomics hold promise for identifying replicable molecular pathways in adults living with FTLD. 'Hub' proteins driving co-expression of affected modules warrant further attention as candidate biomarkers and therapeutic targets.

Figure 1. Study Overview.

Cerebrospinal fluid (CSF) was collected in 116 carriers of autosomal dominant mutations for frontotemporal lobar degeneration (FTLD; 47 C9orf72 [C9], 32 GRN, 37 MAPT) and 39 non-carrier controls with a family history of genetic FTLD. CSF was analyzed on a modified aptamer-based assay (SomaScan). After data processing, a total of 4,138 proteins were quantified. High-dimensional proteomic data were organized into modules of protein co-expression using weighted gene co-expression network analysis (WGCNA). CSF protein co-expression modules from the genetic FTLD network were functionally annotated using gene set and cell type enrichment approaches. CSF genetic FTLD modules were examined in relation to cross-sectional (CDR^®+NACC-FTLD, CSF neurofilament light [NfL]) and longitudinal (global cognitive trajectories) indicators of disease severity. In cross-cohort and cross-platform validation analyses, genetic FTLD modules were reconstructed in independent cohorts of sporadic PSP-Richardson syndrome (PSP-RS) and controls (4RTNI; SomaScan) and FTLD, AD, and controls (BioFINDER 2; Olink).

Figure 2. Genetic FTLD Protein Co-Expression Network.

A) A cerebrospinal fluid (CSF) protein co-expression network was built using weighted gene correlational network analysis (WGCNA). The genetic FLTD network consisted of 31 protein co-expression modules. Module relatedness is shown in the dendrogram to the right. GO analysis was used to identify the principal biology represented by each module. Within genes, module eigenproteins in symptomatic (Sx) and presymptomatic (PreSx) variant carriers were compared against controls. Increased eigenprotein abundance in FTLD is indicated in green, whereas decreased eigenprotein abundance is indicated in blue. Module eigenproteins were correlated with disease outcomes, including CDR^®+NACC-FTLD, global cognitive slope, and CSF NfL (red, positive correlation; blue, negative correlation). The cell type nature of each module was assessed by module protein overlap with cell-type-specific marker lists of neurons, oligodendrocytes, astrocytes, microglia and endothelia. Asterisks in the left heatmap indicate statistical significance after Tukey’s test. Asterisks in the middle and right heatmaps indicate statistical significance after false discovery rate correction. B) Module eigenprotein levels by case status for six of the most strongly FTLD-associated modules. Mutation carriers are grouped by Sx and PreSx individuals. C9: 24 PreSx, 23 Sx; GRN: 12 PreSx, 19 Sx; MAPT: 18 PreSx, 19 Sx. Differences in module eigenprotein by case status were assessed by one-way ANOVA with Tukey test. Gene-specific p-values represent the omnibus significance for gene-stratified comparisons vs. controls. Box plots represent the median and 25th and 75th percentiles, and box hinges represent the interquartile range of the two middle quartiles within a group. Min and max data points define the extent of whiskers (error bars). CTL, control.

Figure 3. Module and Hub Protein Relationships with Cognitive Trajectory.

Plots display the top 5 CSF genetic FTLD network modules most strongly associated with global cognitive trajectories in the full sample. Eigenprotein z-scores are plotted against the annual rate of global cognitive change during the study period (n = 137). Person-specific cognitive slopes were extracted from linear mixed-effects models that included baseline demographics (age, sex, education) and time (years since baseline). Confidence bands represent 95% confidence intervals for Spearman’s r values. Information on the association between all network module eigenproteins and cognitive trajectory in the full sample, within each gene, and within presymptomatic mutation carriers is provided in Supplementary Table 7. For proteins assigned to each module, an individual protein’s strength of connectivity to the module (x-axis) is plotted against the individual protein’s correlation with global cognitive change (y-axis). Proteins that exhibited stronger intramodular connectivity also exhibited stronger relationships with cognitive slope. Color-filled triangles represent individual proteins that survived false discovery rate (FDR) correction in proteome-wide differential correlational analyses (n=646 total proteins, full list in Supplementary Table 7). Proteins in the top 20^th percentile of intramodular connectivity and are classified as ‘hub’ proteins. Hub proteins that significantly correlated with cognitive trajectory are listed with each plot.

Figure 4. Cross-Cohort Validation.

Validation cohorts included 4RTNI, composed of PSP and controls, and BioFINDER 2, composed of patients with frontotemporal dementia clinical syndromes, biomarker-confirmed Alzheimer’s disease (AD), and controls. 4RTNI CSF samples were assayed with SomaScan and BioFINDER CSF samples were assayed with Olink. A) Weighted gene correlational network analysis was applied to 4RTNI SomaScan data to test for module preservation across the genetic FTLD and sporadic PSP-Richardson syndrome (PSP-RS) networks. Modules that have a Z_summary score greater than or equal to 1.96 (or q = 0.05, blue dotted line) are considered to be preserved, and modules that have a Z_summary score greater than or equal to 10 (or q = 1 × 10⁻²³, red dotted line) are considered to be highly preserved. All modules in the genetic FTLD network were highly preserved in the sporadic PSP-RS network. B) Synthetic eigenproteins were reconstructed in 4RTNI and BioFINDER 2 to test for concordance in module relationships with disease groups. Heatmap displays average synthentic eigenprotein z-score differences between PSP-RS and controls (CTL), FTLD vs. CTL, FTLD vs. AD, and AD vs. CTL. Asterisks indicate statistical significance after false discovery rate correction C, D) Synthetic eigenprotein boxplots for key modules from 4RTNI (C) and BioFINDER 2 (D) analyses. Pairwise differences in module synthetic eigenproteins by case status were assessed by two-sided t-test with FDR-correction. Box plots represent the median and 25th and 75th percentiles, and box hinges represent the interquartile range of the two middle quartiles within a group. Min and max data points define the extent of whiskers (error bars).

Figure 5. Genetic FTLD CSF Network Module Over-Representation Analysis with AD CSF Networks.

Module member overrepresentation analysis (ORA) of the genetic FTLD CSF network with two CSF protein co-expression networks in Alzheimer’s disease (AD): 1) 3-platform network, obtained using SomaScan, Olink, and tandem mass tag-based mass spectrometry (TMT-MS; Dammer et al.); 2) single platform network obtained using TMT-MS (Modeste et al.). Box values represent −log₁₀(FDR) value for pairwise module overlap, determined using one-tailed Fisher’s exact test. Bolded AD network modules significantly differed between AD and controls. Modules from the AD networks (y-axis rows) without an overlap value of − log₁₀(FDR) > 1 are not included in the heatmaps.